Date of Award
Summer 8-2011
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Zhu, Dongxiao
Second Advisor
Tu, Shengru
Third Advisor
Taylor, Christopher
Abstract
The main theme of this thesis research is concerned with developing a computational pipeline for processing Next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate tens of millions of short reads for each DNA/RNA sample. The alignment of a large volume of short reads to a reference genome is a key step in NGS data analysis. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing useful information. In order to assist biomedical researchers to conveniently access essential information from NGS data files in SAM/BAM format, we have developed a Graphical User Interface (GUI) software tool named SAMMate to pipeline human transcriptome quantification. SAMMate allows researchers to easily process NGS data files in SAM/BAM format and is compatible with both single-end and paired-end sequencing technologies. It also allows researchers to accurately calculate gene expression abundance scores.
Recommended Citation
Xu, Guorong, "Computational Pipeline for Human Transcriptome Quantification Using RNA-seq Data" (2011). University of New Orleans Theses and Dissertations. 343.
https://scholarworks.uno.edu/td/343
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.